Using modern data mining techniques based on disease management protocols is a powerful way to identify patients who are overdue for care. With a reliable list of such patients, patients can be efficiently contacted and invited to make an appointment, improving patient care while increasing utilization at, and the financial performance of, the medical practice. However, for practices that receive a large volume of patients by referral, anything that damages trust with referring providers can shut off the flow of referrals and compromise the value of their reactivation program. In cases where patients are referred to specialists and expected to return to the referring provider for routine care after the specialty work is completed, contacting a patient for routine care after treating the patient's referred condition is tantamount to stealing the referred patient, and can destroy trust.
Previous solutions to the referral provider problem have focused on recording and displaying referring provider information. This leads to three problems: First, when contact is handled by an automated system, such as an automated telephone message system or by generating a mailing or email list, this information is simply ignored. Second, when a human is presented with the information, the human may fail to notice that the provider is on a do-not-contact list, and make contact anyway. Third, when multiple referring providers are recorded for a single patient, only one may be displayed, possibly giving preference by referral date or some other criteria without proper heed to protected provider status. With incomplete information displayed, the caller is sure to contact patients belonging to referring providers.